Measuring Coping in Parents of Children with Disabilities: A Rasch ...

3 downloads 0 Views 673KB Size Report
Mar 2, 2015 - Parents of a child with disability must cope with greater demands than those ..... scales of CHIP in parents of children with disabilities in India.

Measuring Coping in Parents of Children with Disabilities: A Rasch Model Approach Vijaya K. Gothwal1*, Seelam Bharani1, Shailaja P. Reddy2 1 Meera and L B Deshpande Centre for Sight Enhancement, L V Prasad Eye Institute, Hyderabad, India, 2 Bausch and Lomb School of Optometry, L V Prasad Eye Institute, Hyderabad, India * [email protected]

Abstract Background

OPEN ACCESS Citation: Gothwal VK, Bharani S, Reddy SP (2015) Measuring Coping in Parents of Children with Disabilities: A Rasch Model Approach. PLoS ONE 10 (3): e0118189. doi:10.1371/journal.pone.0118189 Academic Editor: Jan L. Wallander, Merced, UNITED STATES Received: September 11, 2014 Accepted: January 5, 2015 Published: March 2, 2015 Copyright: © 2015 Gothwal et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are included within the paper and its Supporting Information. Funding: This study was supported by the Hyderabad Eye Research Foundation, Hyderabad, India. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist.

Parents of a child with disability must cope with greater demands than those living with a healthy child. Coping refers to a person’s cognitive or behavioral efforts to manage the demands of a stressful situation. The Coping Health Inventory for Parents (CHIP) is a well-recognized measure of coping among parents of chronically ill children and assesses different coping patterns using its three subscales. The purpose of this study was to provide further insights into the psychometric properties of the CHIP subscales in a sample of parents of children with disabilities.

Methods In this cross-sectional study, 220 parents (mean age, 33.4 years; 85% mothers) caring for a child with disability enrolled in special schools as well as in mainstream schools completed the 45-item CHIP. Rasch analysis was applied to the CHIP data and the psychometric performance of each of the three subscales was tested. Subscale revision was performed in the context of Rasch analysis statistics.

Results Response categories were not used as intended, necessitating combining categories, thereby reducing the number from 4 to 3. The subscale – ‘maintaining social support’ satisfied all the Rasch model expectations. Four item misfit the Rasch model in the subscale – maintaining family integration’, but their deletion resulted in a 15-item scale with items that fit the Rasch model well. The remaining subscale – ‘understanding the healthcare situation’ lacked adequate measurement precision (1.3 (Tables 1 and 3). The misfitting items did not appear to be in tandem with the remaining items in the measurement of the underlying construct. Consequently, these four items were removed iteratively, resulting in a 15-item subscale (Table 3). Removal of these items improved the fit of the scale to the Rasch model and the infit MnSq was within the acceptable range for the remaining 15 items. Thus the 15-item “maintaining family integration, co-operation, and an optimistic definition of the situation” subscale possessed a PSR 0.84), implying adequate reliability (i.e. measurement precision). Targeting was 0.65 logits, suggesting that the items were matched well with the participant’s coping patterns. The unexplained variance explained by the first contrast was 2.3 eigenvalue units, indicating unidimensionality. No item displayed notable DIF (Table 3). Rather than discard the four deleted items, we assessed if they could be used to form a valid subscale. However they could not form a valid measure.

Overall performance of maintaining social support, self-esteem, and psychological stability subscale All the 18 items fit the Rasch model well and the PSI was 2.02 (reliability = 0.80), implying adequate measurement precision (Table 3). Targeting was optimal (0.44 logits). The unexplained

PLOS ONE | DOI:10.1371/journal.pone.0118189 March 2, 2015

7 / 17

Coping in Parents

Table 2. Sociodemographic characteristics of parents of children with disabilities who responded to the Coping Health Inventory for Parents (n = 220). Characteristic


Parent Age (years) Mean ± SD

33.4 ± 7.1



Gender, n (%) Female Families with > 1 disabled child, n (%)

187 (85) 35 (15)

Education, n (%) Illiterate/elementary school only

44 (20)

High school/college/university

176 (80)

Marital status, n (%) Married

209 (95)


11 (5)

Employment status, n (%) Not working

152 (69)

Family structure, n (%) Nuclear

133 (60)


87 (40)

Household income (INR) *, n (%) ≤10,000

139 (63)


81 (37)

Care recipient (Child) Age (years) Mean ± SD

9.5 ± 4.2



Gender, n (%) Male

143 (65)

Type of disability Visual

66 (30)


61 (28)

Cerebral palsy

15 (7)


44 (20)

Genetic syndromes

17 (8)

Global developmental disabilities

11 (5)

Attention deficit hyperactivity disorder

4 (2)


2 (1)

Time elapsed since diagnosis of child’s disability (years) Mean ± SD

7.5 ± 4.0



INR—Indian rupees doi:10.1371/journal.pone.0118189.t002

PLOS ONE | DOI:10.1371/journal.pone.0118189 March 2, 2015

8 / 17

Coping in Parents

Fig 1. Rasch model category probability curves for all items together in the CHIP showing the likelihood that a participant with a particular coping ability will select a category. The scale (x-axis) from +6 to-6 symbolizes the latent trait of coping and the y-axis represents the probability of category being selected. Response categories: 0 “not helpful”, 1 “minimally helpful”, 2 “moderately helpful”, and 3 “extremely helpful”. For any given point along this scale, the category most likely to be chosen by a participant is shown by the category curve with the highest probability. At no point, was category 2 the most likely to be chosen, resulting in disordered thresholds (A). Thresholds represent boundaries along the scale where the probability of a response category being chosen changes from one to the next. However, combining categories 1 and 2, and thereby reducing the number of categories from 4 to 3 repaired the disordered thresholds for the category probability curves (B). doi:10.1371/journal.pone.0118189.g001

PLOS ONE | DOI:10.1371/journal.pone.0118189 March 2, 2015

9 / 17

Coping in Parents

Table 3. Overall performance of the CHIP Subscales in parents of children with disabilities. Parameter

Ideal values

Versions of CHIP Subscales MaintainingFamily integration

Maintaining social support

Understanding health care situation



Number of items






No. of misfitting items






Person separation












Mean item location






Mean person location






Principal components analysis (eigenvalue)






Differential item functioning, DIF (Number of items with notable DIF, >1.0 logits)






CHIP- Coping Health Inventory for Parents. * Misfitting items were deleted iteratively and the final 15-item revised version is only shown here. See text for details (results section). doi:10.1371/journal.pone.0118189.t003

variance explained by the first contrast was 2.7 eigenvalue units that satisfied the requirements for unidimensionality. No item displayed notable DIF.

Overall performance of understanding the healthcare situation through communication with other parents and consultation with the healthcare team subscale All the eight items fit the Rasch model well. However the PSI was 1.27 (reliability = 0.62) which is much lower than the minimum accepted value of 2.0, rendering it dysfunctional (Table 3). The unexplained variance explained by the first contrast was 1.7 eigenvalue units. None of the items displayed notable DIF.

Discussion This study provides new insights into the measurement psychometric properties of the subscales of CHIP in parents of children with disabilities in India. Using Rasch analysis, our results have demonstrated that the two revised subscales (“maintaining family integration, co-operation, and an optimistic definition of the situation subscale”, and “maintaining social support, self-esteem, and psychological stability”) of CHIP are unidimensional in our sample. However the subscale “understanding the healthcare situation through communication with other parents and consultation with the healthcare team” appears to be affected by inadequate measurement precision, thereby, rendering it dysfunctional in our population. If our revised response category format for the two functional subscales of CHIP is confirmed in an independent study, then this would have two important implications. Firstly, the use of summary or total subscale scores is justified for the two CHIP subscales (“maintaining family integration” and “maintaining social support”) and secondly, raw subscale scores can easily and legitimately be transformed into interval-scale estimates for these two subscales. Hence, researchers intending to calculate change scores or use parametric statistics can be confident that the transformed data satisfy this criterion [33].

PLOS ONE | DOI:10.1371/journal.pone.0118189 March 2, 2015

10 / 17

Coping in Parents

The two CHIP subscales (“maintaining family integration” and “maintaining social support”) constitute a unidimensional hierarchical scale in which the individual item scores can now justifiably be summed to obtain a reliable summary index of parental coping patterns. Furthermore, the fact that both the subscales were unidimensional indicated that all the items in the respective subscales were in tandem regarding their contribution to the underlying construct (i.e., coping). However we used a lenient criterion in our analysis of unidimensionality given that we were re-validating a legacy instrument (versus developing a new instrument), so it is plausible that there is still some amount of multidimensionality in this subscale. Nonetheless, we do not believe this should be interpreted as a central critique of the two functional subscales of CHIP. As stated earlier, unidimensionality is an important prerequisite for summating any set of Likert-style items [42, 43] commonly seen in instruments used in health care, and therefore, constitutes an important advantage if meaningful measurement is to be obtained [43, 44]. Both the subscales demonstrated high measurement precision (high PSR) suggesting that the parameter estimates obtained in the present study are reproducible and useful for differentiating three groups of participants based on their performance. Both these subscales of CHIP can either be used alone or together to assess two separate coping patterns in parents of children with disabilities in a different cultural setting, i.e., among non-English (e.g., local Indian language) speaking populations. Rasch analysis revealed the presence of four misfitting items (‘believing that my children will get better’, ‘doing things with my children’, ‘working, outside employment’, and ‘talking to someone about how I feel’) in one of the subscales (“maintaining family integration”‘) in this study. However the item misfit was relatively small with largest misfit being an item with infit MnSq, 1.46 (‘talking to someone about how I feel’). Item misfit indicates that the participants responded to these items differently than what the Rasch model expected. Unexpected responses or item misfit can occur for several reasons such as poorly constructed items, ambiguous wording, etc. In the present study, the reason for the four items to misfit (i.e. underfit) was perhaps because these items appear to be measuring another construct from the remaining items. Their deletion also resulted in a reduction in the length of the subscale which in turn reduces respondent burden. It is also interesting to note that the rating scale required revisions (Fig. 1 A and B). The application of Rasch analysis to the CHIP has allowed greater scrutiny of the performance of the rating scale that would not have been possible with the traditional approach to instrument development (i.e., CTT). The response categories proposed for the original CHIP (English version) were found unsuitable for use in the Indian parents of children with disabilities. Our participants found it difficult to distinguish between the two intermediate categories—‘minimally helpful’ and ‘moderately helpful’. These categories were underutilized (used only 17% of the times) as compared to 40% for the end categories. Furthermore, the distance between these two categories was not sufficiently different and was 0.29 logits (which is less than the recommendation by Linacre of at least 1.4 logits apart [45]). Taken together, these findings suggested that we combine the intermediate categories so we performed post-hoc category re-organization to achieve optimal functioning of the rating scale, and we found a revised three-category rating scale (‘not helpful’, ‘somewhat helpful’, ‘extremely helpful’) to be adequate for the items as compared to the originally proposed four-category scale. While threshold disordering can be visualized on the CPC during Rasch analysis indicating the need for category re-organization, it is also important to note that it perhaps makes more sense and is less confusing to the participants when subtle differences such as between ‘minimal’ and ‘moderate’ do not exist in a response category format; rather such nearby categories should be combined and presented as a single category. More importantly, such reduction in number of categories also reduces the cognitive load on the respondent and makes it easier for them to complete the questionnaire.

PLOS ONE | DOI:10.1371/journal.pone.0118189 March 2, 2015

11 / 17

Coping in Parents

The need for collapsing adjacent categories and the resultant category re-organization is not uncommon in instruments with many response options, or when the labeling of options is too similar to each other, which can be confusing or open to misinterpretation. Our finding is consistent with other instruments, for example, vision-specific instruments that have benefitted from a shortening of their ratings scales following Rasch analysis. [18, 46–48]Issues with response categories can occur when the labeling of the response options is ambiguous or too many response options have been included. However, our findings of category re-organization may be a function of the distinct study population and not the measure itself. The need for such post-hoc category revision has been reported for other instruments that have been re-validated using Rasch analysis, for example, visual disability instruments, which have found that the participants are not always able to distinguish between finer increments (for example, between ‘more difficult’ and ‘a lot more difficult’) in response options [18, 49]. However, problems with rating scale structure of the Indian CHIP may be influenced by the relatively small sample used in this study and will need to be verified in larger and broader samples before specific recommendations can be made. Ideally this would include the administration of the original and revised versions of the CHIP scoring to the same people to compare their validity”. Rasch analysis places the items and persons along the same scale, enabling simultaneous comparison of item endorsability and person’s coping patterns (Fig. 2). This feature is lacking in the CTT methodology [50, 51]. For a valid measurement, in addition to acceptable measurement precision, it must possess certain attributes, including adequate spread along the dimension of measurement, and negligible floor and ceiling effects [17]. While the subscale—“maintaining social support” had excellent targeting (0.44 logits), the items in the subscale—“maintaining family integration” were also reasonably well targeted to the participant’s coping patterns (0.65 logits). Furthermore, these two subscales did not demonstrate any DIF with regard to parent gender and child’s type of disability suggesting that it performs equally across mothers and fathers, and across different types of disability. By comparison, the subscale—“understanding the healthcare situation” was dysfunctional primarily because of insufficient items (eight as compared to 19 and 18 in the other two subscales) resulting in poor person separation reliability. However, the low reliability is inconsistent with the original development study that reported relatively high reliability (0.71) for this subscale, albeit using the traditional measure, Cronbach alpha [14]. Nonetheless, Cronbach alpha is used as a reliability coefficient to represent the unidimensionality of an instrument, often exaggerated by the number of items in the instrument [42]. This limitation highlights the need for use of Rasch analysis either in the development [17, 37, 38, 52] or in the re-validation phase of the instruments [23, 24, 48, 53, 54]. As noted earlier, reliability and unidimensionality are fundamental to the Rasch models [17, 44]. Although this subscale lacked reliability, it was unidimensional. However, unidimensionality in the absence of reliability would be of limited value because we may be measuring too coarsely. That is, this subscale cannot discriminate among participants with varying coping patterns in terms of “understanding healthcare situation” effectively. All it can do is to separate the participants into those who consider this coping pattern as ‘not helpful’ and ‘extremely useful’ category. Such low measurement precision limits the usefulness of the subscale in the clinic as it does not help supplement the results of a clinical evaluation. We acknowledge that our study has three important limitations. First, our study included a slightly higher proportion of women (mothers), and literate parents belonging to urban areas. However, this should not compromise the calibration of the instrument. Unlike the calibration of the instrument in the traditional test design which are dependent upon the sample, Rasch analysis allows relatively sample-free test calibration [55]. Second, the data in the present study were collected from those parents whose children were attending school, so it is unknown

PLOS ONE | DOI:10.1371/journal.pone.0118189 March 2, 2015

12 / 17

Coping in Parents

Fig 2. Person-item map for the Rasch-revised 15-item ‘maintaining family integration, co-operation, and an optimistic definition of the situation’ subscale of the Coping Health Inventory for Parents (n = 220). Participants are located on the left of the dashed line (represented by ‘x’) and participants with better coping ability are located at the top of the map. Items (i.e., coping patterns) are on the right of the dashed line with those considered to be least helpful located toward the top of the map. Each ‘x’ and “.” represent two and one participants respectively. Alongside each item is also indicated its abridged description and number as in the 45-item original CHIP. The complete description of items can be found in Table 2 in the text. M, mean; S, 1 SD from the mean; T, 2 SD from the mean. doi:10.1371/journal.pone.0118189.g002

PLOS ONE | DOI:10.1371/journal.pone.0118189 March 2, 2015

13 / 17

Coping in Parents

whether the results can be generalized to parents whose child (ren) with disability do not attend school, especially those residing in rural areas. Hence future studies should apply the CHIP to parents of children with disabilities who are out of school (especially in rural areas) and to evaluate whether DIF exists among such parents. In conclusion, our study provides new insights into measurement properties of CHIP and the Rasch analyses reported here have added another perspective to the instrument, providing a tentative view of the strengths and weaknesses of its subscales. Two subscales, i.e., revised—“maintaining family integration” and original “maintaining social support”) are unidimensional measurement scales that are efficiently administered and are appropriately targeted for the assessment of coping patterns in parents of children with disabilities. However future studies utilizing larger samples should be undertaken using Rasch analysis to confirm the findings of this study. Broader samples, including a wider variety of children with disabilities drawn from different settings, should be utilized. Further evaluation of the response category format of the instrument should be undertaken to examine the decision made in the study to rescore the categories of the items in the CHIP. Ideally this would include the administration of the original and revised version of the CHIP scoring to the same people to compare their validity. Further research could also examine the longitudinal psychometric properties such as reproducibility and responsiveness to change over time following an intervention (e.g., parental support network, counselling, etc.) of the CHIP. Both the functional subscales are useful outcome measures for large public health studies and in evaluating the impact of interventions, for example, in counseling services for parents of children with disabilities.

Supporting Information S1 Data (XLSX)

Acknowledgments The authors of this study are especially grateful to the parents of children with disabilities who volunteered to participate and gave their valuable time. We want to thank the principals of the schools who provided us access to the parents of children with disabilities who were enrolled in their schools.

Author Contributions Conceived and designed the experiments: VKG. Performed the experiments: SB SPR VKG. Analyzed the data: VKG SB SPR. Contributed reagents/materials/analysis tools: VKG SB SRP. Wrote the paper: VKG.

References 1.

Ceylan R, Aral N (2007) An examination of the correlation between depression and hopelessness levels in mothers of disabled children. Soc Behav Pers 35: 903–908.


McCubbin MA, McCubbin HI (1987) Family stress theory and assessment: the T-double ABCX model of family adjustment and adaptation. Madison, WI, USA: University of Wisconsin-Madison. PMID: 25032476


Silver EJ, Westbrook LE, Stein RE (1998) Relationship of parental psychological distress to consequences of chronic health conditions in children. J Pediatr Psychol 23: 5–15. PMID: 9564123


Murphy NA, Christian B, Caplin DA,Young PC (2007) The health of caregivers for children with disabilities: caregiver perspectives. Child Care Health Dev 33: 180–187. PMID: 17291322


Harris SL (1994) Topics in Autism: Siblings of Children with Autism: A Guide for Families. Bethesda, MD: Woodbine House. PMID: 25144107

PLOS ONE | DOI:10.1371/journal.pone.0118189 March 2, 2015

14 / 17

Coping in Parents


Lazarus RS,Folkman S (1984) Stress, appraisal, and coping New York, NY: Springer. PMID: 25057717


Abbeduto L, Seltzer MM, Shattuck P, Krauss MW, Orsmond G, et al. (2004) Psychological well-being and coping in mothers of youths with autism, Down syndrome, or fragile X syndrome. Am J Ment Retard 109: 237–254. PMID: 15072518


Fox RA, Platz DL, Bentley KS (1995) Maternal factors related to parenting practices, developmental expectations, and perceptions of child behavior problems. J Genet Psychol 156: 431–441. PMID: 8543930


Haveman M, Van Berkum C, Reijinders R, Heller T (1997) Differences in service needs, time demands, and caregiving burden among parents of persons with mental retardation across the life cycle. Family Relations 46: 417–425.


Roy KM, Dyson O (2010) Making daddies into fathers: community-based fatherhood programs and the construction of masculinities for low-income African American men. Am J Community Psychol 45: 139–154. doi: 10.1007/s10464-009-9282-4 PMID: 20077133


Trout MD (1990) Child and parental predictors of family adjustment in households containing young developmentally disabled children. Family Relations 39: 292–297.


Glidden LM, Billings FJ, Jobe BM (2006) Personality, coping style and well-being of parents rearing children with developmental disabilities. J Intellect Disabil Res 50: 949–962. PMID: 17100955


Cavallo S, Feldman DE, Swaine B,Meshefedjian G (2009) Is parental coping associated with the level of function in children with physical disabilities? Child Care Health Dev 35: 33–40. doi: 10.1111/j.13652214.2008.00884.x PMID: 19523000


McCubbin HI, McCubbin MA, Patterson JM, Cauble AE, Wilson LR, et al. (1983) CHIP—Coping Health Inventory for parents: an assessment of parental coping patterns in the care of the chronically ill child. J Marriage and Family 45: 359–370.


Garro A (2004) Coping patterns in mothers/caregivers of children with chronic feeding problems. J Pediatr Health Care 18: 138–144. PMID: 15129214


Garro A (2011) Coping patterns in Latino families of children with asthma. J Pediatr Health Care 25: 347–354. doi: 10.1016/j.pedhc.2010.04.005 PMID: 22018425


Pesudovs K, Burr JM, Harley C, Elliott DB (2007) The development, assessment, and selection of questionnaires. Optom Vis Sci 84: 663–674. PMID: 17700331


Gothwal VK, Wright TA, Lamoureux EL, Pesudovs K (2010) Activities of Daily Vision Scale: what do the subscales measure? Invest Ophthalmol Vis Sci 51: 694–700. doi: 10.1167/iovs.09-3448 PMID: 19684011


Svensson E (2001) Guidelines to statistical evaluation of data from rating scales and questionnaires. J Rehabil Med 33: 47–48. PMID: 11480471


Desai P, Minassian DC, Reidy A (1999) National cataract surgery survey 1997–8: a report of the results of the clinical outcomes. Br J Ophthalmol 83: 1336–1340. PMID: 10574810


Desai P, Reidy A, Minassian DC, Vafidis G, Bolger J (1996) Gains from cataract surgery: visual function and quality of life. Br J Ophthalmol 80: 868–873. PMID: 8976696


Massof RW, Rubin GS (2001) Visual function assessment questionnaires. Surv Ophthalmol 45: 531– 548. PMID: 11425359


Gothwal VK, Wright TA, Lamoureux EL,Pesudovs K (2009) Cataract Symptom Scale: clarifying measurement. Br J Ophthalmol 93: 1652–1656. doi: 10.1136/bjo.2009.159806 PMID: 19692374


Gothwal VK, Wright TA, Lamoureux EL, Pesudovs K (2009) Using Rasch analysis to revisit the validity of the Cataract TyPE Spec instrument for measuring cataract surgery outcomes. J Cataract Refract Surg 35: 1509–1517. doi: 10.1016/j.jcrs.2009.03.056 PMID: 19683146


Gothwal VK, Wright TA, Lamoureux EL, Pesudovs K (2009) Rasch analysis of the quality of life and vision function questionnaire. Optom Vis Sci 86: E836–844. doi: 10.1097/OPX.0b013e3181ae1ec7 PMID: 19521266


Pallant JF,Tennant A (2007) An introduction to the Rasch measurement model: an example using the Hospital Anxiety and Depression Scale (HADS). Br J Clin Psychol 46: 1–18. PMID: 17472198


Rasch G (1980) Probabilistic models for some intelligence and attainment test. Chicago: University of Chicago Press. PMID: 25121255


Bond TG, Fox CM (2001) Applying the Rasch model: Fundamental Measurement in the Human Sciences. London: Lawrence Erlbaum Associates. PMID: 25506954


Hays RD, Morales LS, Reise SP (2000) Item response theory and health outcomes measurement in the 21st century. Med Care 38: II28–42. PMID: 10982088

PLOS ONE | DOI:10.1371/journal.pone.0118189 March 2, 2015

15 / 17

Coping in Parents


Dorans NJ, Kulick E (2006) Differential item functioning on the Mini-Mental State Examination. An application of the Mantel-Haenszel and standardization procedures. Med Care 44: S107–114. PMID: 17060817


Norquist JM, Fitzpatrick R, Dawson J, Jenkinson C (2004) Comparing alternative Rasch-based methods vs raw scores in measuring change in health. Med Care 42: I25–36. PMID: 14707753


Garamendi E, Pesudovs K, Stevens MJ, Elliott DB (2006) The Refractive Status and Vision Profile: evaluation of psychometric properties and comparison of Rasch and summated Likert-scaling. Vision Res 46: 1375–1383. PMID: 16105674


Cavallo S, Feldman DE, Swaine B, Meshefedjian G, Malleson PN, et al. (2009) Is parental coping associated with quality of life in juvenile idiopathic arthritis? Pediatr Rheumatol Online J 7: 7. doi: 10.1186/ 1546-0096-7-7 PMID: 19284585


Guillemin F, Bombardier C, Beaton D (1993) Cross-cultural adaptation of health-related quality of life measures: literature review and proposed guidelines. J Clin Epidemiol 46: 1417–1432. PMID: 8263569


Andrich D (1978) A rating scale formulation for ordered response categories. Psychometrika 43: 561– 573.


Linacre JM (2012) A User's Guide to Winsteps: Rasch Model Computer Program. Chicago: Winsteps. com. doi: 10.1007/s12070-012-0514-9 PMID: 25621271


Gothwal VK, Sumalini R, Bharani S, Reddy SP, Bagga DK (2012) The second version of the L. V. Prasad-functional vision questionnaire. Optom Vis Sci 89: 1601–1610. doi: 10.1097/OPX. 0b013e31826ca291 PMID: 23069725


Marella M, Gothwal VK, Pesudovs K, Lamoureux E (2009) Validation of the visual disability questionnaire (VDQ) in India. Optom Vis Sci 86: E826–835. doi: 10.1097/OPX.0b013e3181ae1b3f PMID: 19543138


Eakman AM (2012) Measurement characteristics of the engagement in meaningful activities survey in an age-diverse sample. Am J Occup Ther 66: e20–29. doi: 10.5014/ajot.2012.001867 PMID: 22394537


Wright BD, Douglas GA (1975) Best test design and self-tailored testing. Chicago, IL: Statistical laboratory, Department of Education, University of Chicago. PMID: 25077283


Wright BD, Douglas GA (1976) Rasch item analysis by hand. Chicago, IL: Statistical Laboratory, Department of Education, University of Chicago. PMID: 25032409


Streiner DL, Norman GR (2003) Health measurement scales: A guide to their development and use. New York: Oxford University Press Inc. PMID: 25057689


Tennant A, McKenna SP, Hagell P (2004) Application of Rasch analysis in the development and application of quality of life instruments. Value Health 7 Suppl 1: S22–26. PMID: 15367240


Mallinson T (2007) Why measurement matters for measuring patient vision outcomes. Optom Vis Sci 84: 675–682. PMID: 17700332


Linacre JM (2002) Optimizing rating scale category effectiveness. J Appl Meas 3: 85–106. PMID: 11997586


Lamoureux EL, Pallant JF, Pesudovs K, Hassell JB, Keeffe JE(2006) The Impact of Vision Impairment Questionnaire: an evaluation of its measurement properties using Rasch analysis. Invest Ophthalmol Vis Sci 47: 4732–4741. PMID: 17065481


Pesudovs K, Garamendi E, Keeves JP, Elliott DB (2003) The Activities of Daily Vision Scale for cataract surgery outcomes: re-evaluating validity with Rasch analysis. Invest Ophthalmol Vis Sci 44: 2892– 2899. PMID: 12824228


Velozo CA, Lai JS, Mallinson T, Hauselman E (2000) Maintaining instrument quality while reducing items: application of Rasch analysis to a self-report of visual function. J Outcome Meas 4: 667–680. PMID: 11253902


Pesudovs K, Gothwal VK, Wright T, Lamoureux EL (2010) Remediating serious flaws in the National Eye Institute Visual Function Questionnaire. J Cataract Refract Surg 36: 718–732. doi: 10.1016/j.jcrs. 2009.11.019 PMID: 20457362


Crocker L, Algina J (1986) Introduction to classical and modern test theory. Fort Worth: Harcourt Brace Jovanovich. PMID: 25101439


DeVellis RF (2006) Classical test theory. Med Care 44: S50–59. PMID: 17060836


Pesudovs K, Garamendi E, Elliott DB (2004) The Quality of Life Impact of Refractive Correction (QIRC) Questionnaire: development and validation. Optom Vis Sci 81: 769–777. PMID: 15557851


Lamoureux EL, Pesudovs K, Pallant JF, Rees G, Hassell JB, et al. (2008) An evaluation of the 10-item vision core measure 1 (VCM1) scale (the Core Module of the Vision-Related Quality of Life scale) using

PLOS ONE | DOI:10.1371/journal.pone.0118189 March 2, 2015

16 / 17

Coping in Parents

Rasch analysis. Ophthalmic Epidemiol 15: 224–233. doi: 10.1080/09286580802256559 PMID: 18780255 54.

Pesudovs K, Caudle LE, Rees G,Lamoureux EL (2008) Validity of a visual impairment questionnaire in measuring cataract surgery outcomes. J Cataract Refract Surg 34: 925–933. doi: 10.1016/j.jcrs.2007. 12.052 PMID: 18498997


Wright BD (1979) Best Test Design: Rasch Measurement. Chicago: MESA Press. PMID: 25121236

PLOS ONE | DOI:10.1371/journal.pone.0118189 March 2, 2015

17 / 17